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Nonlinear Coupling in the Human Motor System.

Authors :
Chun-Chuan Chen
Kilner, James M.
Friston, Karl J.
Kiebel, Stefan J.
Jolly, Rohit K.
Ward, Nick S.
Source :
Journal of Neuroscience; 6/23/2010, Vol. 30 Issue 25, p8393-8399, 7p, 1 Diagram, 1 Chart, 3 Graphs
Publication Year :
2010

Abstract

The synchronous discharge of neuronal assemblies is thought to facilitate communication between areas within distributed networks in the human brain. This oscillatory activity is especially interesting, given the pathological modulation of specific frequencies in diseases affecting the motor system. Many studies investigating oscillatory activity have focused on same frequency, or linear, coupling between areas of a network. In this study, our aim was to establish a functional architecture in the human motor system responsible for induced responses as measured in normal subjects with magnetoencephalography. Specifically, we looked for evidence for additional nonlinear (between-frequency) coupling among neuronal sources and, in particular, whether nonlinearities were found predominantly in connections within areas (intrinsic), between areas (extrinsic) or both. We modeled the event-related modulation of spectral responses during a simple hand-grip using dynamic casual modeling. We compared models with and without nonlinear connections under conditions of symmetric and asymmetric interhemispheric connectivity. Bayesian model comparison suggested that the task-dependent motor network was asymmetric during right hand movements. Furthermore, it revealed very strong evidence for nonlinear coupling between sources in this distributed network, but interactions among frequencies within a source appeared linear in nature. Our results provide empirical evidence for nonlinear coupling among distributed neuronal sources in the motor system and that these play an important role in modulating spectral responses under normal conditions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02706474
Volume :
30
Issue :
25
Database :
Complementary Index
Journal :
Journal of Neuroscience
Publication Type :
Academic Journal
Accession number :
51914932
Full Text :
https://doi.org/10.1523/JNEUROSCI.1194-09.2010